Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations56636
Missing cells648773
Missing cells (%)37.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.2 MiB
Average record size in memory1021.5 B

Variable types

Numeric12
Text6
Categorical9
DateTime1
Boolean2
Path1

Alerts

State has constant value "TN" Constant
Bedrooms is highly overall correlated with Finished Area and 1 other fieldsHigh correlation
Building Value is highly overall correlated with Finished Area and 5 other fieldsHigh correlation
City is highly overall correlated with Property CityHigh correlation
Finished Area is highly overall correlated with Bedrooms and 6 other fieldsHigh correlation
Full Bath is highly overall correlated with Bedrooms and 3 other fieldsHigh correlation
Grade is highly overall correlated with Building Value and 4 other fieldsHigh correlation
Land Use is highly overall correlated with Grade and 2 other fieldsHigh correlation
Land Value is highly overall correlated with Building Value and 3 other fieldsHigh correlation
Multiple Parcels Involved in Sale is highly overall correlated with Land Use and 1 other fieldsHigh correlation
Property City is highly overall correlated with CityHigh correlation
Sale Price is highly overall correlated with Building Value and 4 other fieldsHigh correlation
Sold As Vacant is highly overall correlated with Land Use and 1 other fieldsHigh correlation
Total Value is highly overall correlated with Building Value and 5 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Unnamed: 0.1High correlation
Unnamed: 0.1 is highly overall correlated with Unnamed: 0High correlation
Land Use is highly imbalanced (68.1%) Imbalance
Property City is highly imbalanced (58.4%) Imbalance
Sold As Vacant is highly imbalanced (57.6%) Imbalance
Multiple Parcels Involved in Sale is highly imbalanced (58.8%) Imbalance
City is highly imbalanced (65.6%) Imbalance
Tax District is highly imbalanced (61.7%) Imbalance
Exterior Wall is highly imbalanced (51.9%) Imbalance
Grade is highly imbalanced (68.8%) Imbalance
Half Bath is highly imbalanced (53.6%) Imbalance
Suite/ Condo # has 50527 (89.2%) missing values Missing
Owner Name has 31375 (55.4%) missing values Missing
Address has 30619 (54.1%) missing values Missing
City has 30619 (54.1%) missing values Missing
State has 30619 (54.1%) missing values Missing
Acreage has 30619 (54.1%) missing values Missing
Tax District has 30619 (54.1%) missing values Missing
Neighborhood has 30619 (54.1%) missing values Missing
image has 31301 (55.3%) missing values Missing
Land Value has 30619 (54.1%) missing values Missing
Building Value has 30619 (54.1%) missing values Missing
Total Value has 30619 (54.1%) missing values Missing
Finished Area has 32470 (57.3%) missing values Missing
Foundation Type has 32472 (57.3%) missing values Missing
Year Built has 32471 (57.3%) missing values Missing
Exterior Wall has 32471 (57.3%) missing values Missing
Grade has 32471 (57.3%) missing values Missing
Bedrooms has 32477 (57.3%) missing values Missing
Full Bath has 32359 (57.1%) missing values Missing
Half Bath has 32490 (57.4%) missing values Missing
Sale Price is highly skewed (γ1 = 30.49774693) Skewed
Acreage is highly skewed (γ1 = 52.27927679) Skewed
Finished Area is highly skewed (γ1 = 65.94511713) Skewed
Unnamed: 0.1 is uniformly distributed Uniform
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0.1 has unique values Unique
Unnamed: 0 has unique values Unique
Building Value has 1852 (3.3%) zeros Zeros

Reproduction

Analysis started2025-06-20 10:06:36.971136
Analysis finished2025-06-20 10:07:12.861022
Duration35.89 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct56636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28317.5
Minimum0
Maximum56635
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:13.062975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2831.75
Q114158.75
median28317.5
Q342476.25
95-th percentile53803.25
Maximum56635
Range56635
Interquartile range (IQR)28317.5

Descriptive statistics

Standard deviation16349.549
Coefficient of variation (CV)0.57736556
Kurtosis-1.2
Mean28317.5
Median Absolute Deviation (MAD)14159
Skewness0
Sum1.6037899 × 109
Variance2.6730776 × 108
MonotonicityStrictly increasing
2025-06-20T15:37:13.277917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
37762 1
 
< 0.1%
37751 1
 
< 0.1%
37752 1
 
< 0.1%
37753 1
 
< 0.1%
37754 1
 
< 0.1%
37755 1
 
< 0.1%
37756 1
 
< 0.1%
37757 1
 
< 0.1%
37758 1
 
< 0.1%
Other values (56626) 56626
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
56635 1
< 0.1%
56634 1
< 0.1%
56633 1
< 0.1%
56632 1
< 0.1%
56631 1
< 0.1%
56630 1
< 0.1%
56629 1
< 0.1%
56628 1
< 0.1%
56627 1
< 0.1%
56626 1
< 0.1%

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct56636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28317.5
Minimum0
Maximum56635
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:13.485652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2831.75
Q114158.75
median28317.5
Q342476.25
95-th percentile53803.25
Maximum56635
Range56635
Interquartile range (IQR)28317.5

Descriptive statistics

Standard deviation16349.549
Coefficient of variation (CV)0.57736556
Kurtosis-1.2
Mean28317.5
Median Absolute Deviation (MAD)14159
Skewness0
Sum1.6037899 × 109
Variance2.6730776 × 108
MonotonicityStrictly increasing
2025-06-20T15:37:13.685622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
37762 1
 
< 0.1%
37751 1
 
< 0.1%
37752 1
 
< 0.1%
37753 1
 
< 0.1%
37754 1
 
< 0.1%
37755 1
 
< 0.1%
37756 1
 
< 0.1%
37757 1
 
< 0.1%
37758 1
 
< 0.1%
Other values (56626) 56626
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
56635 1
< 0.1%
56634 1
< 0.1%
56633 1
< 0.1%
56632 1
< 0.1%
56631 1
< 0.1%
56630 1
< 0.1%
56629 1
< 0.1%
56628 1
< 0.1%
56627 1
< 0.1%
56626 1
< 0.1%
Distinct48697
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-06-20T15:37:14.125842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.519493
Min length15

Characters and Unicode

Total characters878962
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41452 ?
Unique (%)73.2%

Sample

1st row105 03 0D 008.00
2nd row105 11 0 080.00
3rd row118 03 0 130.00
4th row119 01 0 479.00
5th row119 05 0 186.00
ValueCountFrequency (%)
0 27214
 
12.0%
0a 14081
 
6.2%
0b 5557
 
2.5%
02 4112
 
1.8%
09 3947
 
1.7%
01 3877
 
1.7%
16 3855
 
1.7%
06 3698
 
1.6%
05 3627
 
1.6%
07 3563
 
1.6%
Other values (1207) 153013
67.5%
2025-06-20T15:37:14.784856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 286157
32.6%
169908
19.3%
1 105889
 
12.0%
. 56636
 
6.4%
2 39098
 
4.4%
3 36219
 
4.1%
4 31088
 
3.5%
6 27716
 
3.2%
5 27014
 
3.1%
7 24221
 
2.8%
Other values (28) 75016
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 878962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 286157
32.6%
169908
19.3%
1 105889
 
12.0%
. 56636
 
6.4%
2 39098
 
4.4%
3 36219
 
4.1%
4 31088
 
3.5%
6 27716
 
3.2%
5 27014
 
3.1%
7 24221
 
2.8%
Other values (28) 75016
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 878962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 286157
32.6%
169908
19.3%
1 105889
 
12.0%
. 56636
 
6.4%
2 39098
 
4.4%
3 36219
 
4.1%
4 31088
 
3.5%
6 27716
 
3.2%
5 27014
 
3.1%
7 24221
 
2.8%
Other values (28) 75016
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 878962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 286157
32.6%
169908
19.3%
1 105889
 
12.0%
. 56636
 
6.4%
2 39098
 
4.4%
3 36219
 
4.1%
4 31088
 
3.5%
6 27716
 
3.2%
5 27014
 
3.1%
7 24221
 
2.8%
Other values (28) 75016
 
8.5%

Land Use
Categorical

High correlation  Imbalance 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
SINGLE FAMILY
34268 
RESIDENTIAL CONDO
14081 
VACANT RESIDENTIAL LAND
3586 
VACANT RES LAND
 
1575
DUPLEX
 
1389
Other values (34)
 
1737

Length

Max length42
Median length13
Mean length14.496221
Min length5

Characters and Unicode

Total characters821008
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowRESIDENTIAL CONDO
2nd rowSINGLE FAMILY
3rd rowSINGLE FAMILY
4th rowSINGLE FAMILY
5th rowSINGLE FAMILY

Common Values

ValueCountFrequency (%)
SINGLE FAMILY 34268
60.5%
RESIDENTIAL CONDO 14081
24.9%
VACANT RESIDENTIAL LAND 3586
 
6.3%
VACANT RES LAND 1575
 
2.8%
DUPLEX 1389
 
2.5%
ZERO LOT LINE 1049
 
1.9%
CONDO 252
 
0.4%
RESIDENTIAL COMBO/MISC 95
 
0.2%
TRIPLEX 92
 
0.2%
QUADPLEX 39
 
0.1%
Other values (29) 210
 
0.4%

Length

2025-06-20T15:37:15.037862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
family 34270
29.1%
single 34268
29.1%
residential 17762
15.1%
condo 14368
12.2%
vacant 5185
 
4.4%
land 5183
 
4.4%
res 1575
 
1.3%
duplex 1389
 
1.2%
lot 1060
 
0.9%
zero 1049
 
0.9%
Other values (69) 1735
 
1.5%

Most occurring characters

ValueCountFrequency (%)
I 105515
12.9%
L 95228
11.6%
N 77974
9.5%
E 75227
9.2%
A 67738
 
8.3%
61240
 
7.5%
S 53821
 
6.6%
D 38823
 
4.7%
M 34670
 
4.2%
G 34330
 
4.2%
Other values (28) 176442
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 821008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 105515
12.9%
L 95228
11.6%
N 77974
9.5%
E 75227
9.2%
A 67738
 
8.3%
61240
 
7.5%
S 53821
 
6.6%
D 38823
 
4.7%
M 34670
 
4.2%
G 34330
 
4.2%
Other values (28) 176442
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 821008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 105515
12.9%
L 95228
11.6%
N 77974
9.5%
E 75227
9.2%
A 67738
 
8.3%
61240
 
7.5%
S 53821
 
6.6%
D 38823
 
4.7%
M 34670
 
4.2%
G 34330
 
4.2%
Other values (28) 176442
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 821008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 105515
12.9%
L 95228
11.6%
N 77974
9.5%
E 75227
9.2%
A 67738
 
8.3%
61240
 
7.5%
S 53821
 
6.6%
D 38823
 
4.7%
M 34670
 
4.2%
G 34330
 
4.2%
Other values (28) 176442
21.5%
Distinct45068
Distinct (%)79.8%
Missing159
Missing (%)0.3%
Memory size3.6 MiB
2025-06-20T15:37:15.520768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length17.472812
Min length1

Characters and Unicode

Total characters986812
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39441 ?
Unique (%)69.8%

Sample

1st row1208 3RD AVE S
2nd row1802 STEWART PL
3rd row2761 ROSEDALE PL
4th row224 PEACHTREE ST
5th row316 LUTIE ST
ValueCountFrequency (%)
dr 16534
 
8.7%
ave 11453
 
6.1%
st 4925
 
2.6%
rd 4535
 
2.4%
ct 4134
 
2.2%
ln 3517
 
1.9%
n 2290
 
1.2%
pl 1874
 
1.0%
blvd 1660
 
0.9%
s 1654
 
0.9%
Other values (10736) 136705
72.2%
2025-06-20T15:37:16.157608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
188883
19.1%
E 62508
 
6.3%
R 61066
 
6.2%
A 53255
 
5.4%
D 42289
 
4.3%
L 41157
 
4.2%
1 40590
 
4.1%
N 38226
 
3.9%
O 37489
 
3.8%
T 35837
 
3.6%
Other values (27) 385512
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 986812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
188883
19.1%
E 62508
 
6.3%
R 61066
 
6.2%
A 53255
 
5.4%
D 42289
 
4.3%
L 41157
 
4.2%
1 40590
 
4.1%
N 38226
 
3.9%
O 37489
 
3.8%
T 35837
 
3.6%
Other values (27) 385512
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 986812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
188883
19.1%
E 62508
 
6.3%
R 61066
 
6.2%
A 53255
 
5.4%
D 42289
 
4.3%
L 41157
 
4.2%
1 40590
 
4.1%
N 38226
 
3.9%
O 37489
 
3.8%
T 35837
 
3.6%
Other values (27) 385512
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 986812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
188883
19.1%
E 62508
 
6.3%
R 61066
 
6.2%
A 53255
 
5.4%
D 42289
 
4.3%
L 41157
 
4.2%
1 40590
 
4.1%
N 38226
 
3.9%
O 37489
 
3.8%
T 35837
 
3.6%
Other values (27) 385512
39.1%

Suite/ Condo #
Text

Missing 

Distinct1952
Distinct (%)32.0%
Missing50527
Missing (%)89.2%
Memory size1.8 MiB
2025-06-20T15:37:16.607748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6352922
Min length1

Characters and Unicode

Total characters22208
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique855 ?
Unique (%)14.0%

Sample

1st row8
2nd rowC-10
3rd row144
4th row403
5th row18
ValueCountFrequency (%)
1 55
 
0.9%
2 54
 
0.9%
5 53
 
0.9%
4 51
 
0.8%
3 47
 
0.8%
6 46
 
0.8%
7 46
 
0.8%
8 43
 
0.7%
11 41
 
0.7%
204 40
 
0.7%
Other values (1938) 5635
92.2%
2025-06-20T15:37:17.225506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4934
22.2%
1 3879
17.5%
2 2241
10.1%
. 2096
9.4%
3 1747
 
7.9%
4 1371
 
6.2%
5 1071
 
4.8%
6 886
 
4.0%
- 856
 
3.9%
7 803
 
3.6%
Other values (29) 2324
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4934
22.2%
1 3879
17.5%
2 2241
10.1%
. 2096
9.4%
3 1747
 
7.9%
4 1371
 
6.2%
5 1071
 
4.8%
6 886
 
4.0%
- 856
 
3.9%
7 803
 
3.6%
Other values (29) 2324
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4934
22.2%
1 3879
17.5%
2 2241
10.1%
. 2096
9.4%
3 1747
 
7.9%
4 1371
 
6.2%
5 1071
 
4.8%
6 886
 
4.0%
- 856
 
3.9%
7 803
 
3.6%
Other values (29) 2324
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4934
22.2%
1 3879
17.5%
2 2241
10.1%
. 2096
9.4%
3 1747
 
7.9%
4 1371
 
6.2%
5 1071
 
4.8%
6 886
 
4.0%
- 856
 
3.9%
7 803
 
3.6%
Other values (29) 2324
10.5%

Property City
Categorical

High correlation  Imbalance 

Distinct14
Distinct (%)< 0.1%
Missing159
Missing (%)0.3%
Memory size3.1 MiB
NASHVILLE
40280 
ANTIOCH
6316 
HERMITAGE
 
3133
MADISON
 
2114
BRENTWOOD
 
1696
Other values (9)
 
2938

Length

Max length14
Median length9
Mean length8.8485578
Min length7

Characters and Unicode

Total characters499740
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowNASHVILLE
2nd rowNASHVILLE
3rd rowNASHVILLE
4th rowNASHVILLE
5th rowNASHVILLE

Common Values

ValueCountFrequency (%)
NASHVILLE 40280
71.1%
ANTIOCH 6316
 
11.2%
HERMITAGE 3133
 
5.5%
MADISON 2114
 
3.7%
BRENTWOOD 1696
 
3.0%
OLD HICKORY 1415
 
2.5%
GOODLETTSVILLE 735
 
1.3%
NOLENSVILLE 494
 
0.9%
MOUNT JULIET 183
 
0.3%
WHITES CREEK 97
 
0.2%
Other values (4) 14
 
< 0.1%
(Missing) 159
 
0.3%

Length

2025-06-20T15:37:17.430967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nashville 40280
69.2%
antioch 6316
 
10.9%
hermitage 3133
 
5.4%
madison 2114
 
3.6%
brentwood 1696
 
2.9%
old 1415
 
2.4%
hickory 1415
 
2.4%
goodlettsville 735
 
1.3%
nolensville 494
 
0.8%
mount 183
 
0.3%
Other values (7) 391
 
0.7%

Most occurring characters

ValueCountFrequency (%)
L 85859
17.2%
I 54768
11.0%
A 51844
10.4%
N 51593
10.3%
H 51241
10.3%
E 51188
10.2%
S 43720
8.7%
V 41510
8.3%
O 16822
 
3.4%
T 13089
 
2.6%
Other values (13) 38106
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 85859
17.2%
I 54768
11.0%
A 51844
10.4%
N 51593
10.3%
H 51241
10.3%
E 51188
10.2%
S 43720
8.7%
V 41510
8.3%
O 16822
 
3.4%
T 13089
 
2.6%
Other values (13) 38106
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 85859
17.2%
I 54768
11.0%
A 51844
10.4%
N 51593
10.3%
H 51241
10.3%
E 51188
10.2%
S 43720
8.7%
V 41510
8.3%
O 16822
 
3.4%
T 13089
 
2.6%
Other values (13) 38106
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 85859
17.2%
I 54768
11.0%
A 51844
10.4%
N 51593
10.3%
H 51241
10.3%
E 51188
10.2%
S 43720
8.7%
V 41510
8.3%
O 16822
 
3.4%
T 13089
 
2.6%
Other values (13) 38106
7.6%
Distinct1117
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size442.6 KiB
Minimum2013-01-02 00:00:00
Maximum2016-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-20T15:37:17.635682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:18.163931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sale Price
Real number (ℝ)

High correlation  Skewed 

Distinct8085
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327211.13
Minimum50
Maximum54278060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:18.557917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60000
Q1135000
median205450
Q3329000
95-th percentile759000
Maximum54278060
Range54278010
Interquartile range (IQR)194000

Descriptive statistics

Standard deviation928742.55
Coefficient of variation (CV)2.8383587
Kurtosis1487.6099
Mean327211.13
Median Absolute Deviation (MAD)84550
Skewness30.497747
Sum1.8531929 × 1010
Variance8.6256272 × 1011
MonotonicityNot monotonic
2025-06-20T15:37:18.827114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 556
 
1.0%
200000 441
 
0.8%
160000 435
 
0.8%
120000 411
 
0.7%
125000 405
 
0.7%
175000 399
 
0.7%
130000 394
 
0.7%
140000 390
 
0.7%
165000 379
 
0.7%
135000 372
 
0.7%
Other values (8075) 52454
92.6%
ValueCountFrequency (%)
50 1
 
< 0.1%
100 2
 
< 0.1%
500 1
 
< 0.1%
750 1
 
< 0.1%
800 2
 
< 0.1%
1000 8
< 0.1%
1500 1
 
< 0.1%
2000 2
 
< 0.1%
2500 1
 
< 0.1%
3000 10
< 0.1%
ValueCountFrequency (%)
54278060 7
 
< 0.1%
14100000 23
 
< 0.1%
13156000 92
0.2%
12350000 2
 
< 0.1%
10750000 1
 
< 0.1%
9500000 6
 
< 0.1%
7200000 1
 
< 0.1%
5491000 30
 
0.1%
5200000 1
 
< 0.1%
5000000 3
 
< 0.1%
Distinct52898
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-06-20T15:37:19.185484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length16.000088
Min length15

Characters and Unicode

Total characters906181
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51585 ?
Unique (%)91.1%

Sample

1st row20130128-0008725
2nd row20130118-0006337
3rd row20130124-0008033
4th row20130128-0008863
5th row20130131-0009929
ValueCountFrequency (%)
20150511-0042855 116
 
0.2%
20150202-0009517 92
 
0.2%
20130702-0068072 85
 
0.1%
20140106-0001071 78
 
0.1%
20150513-0043661 72
 
0.1%
20160405-0032679 54
 
0.1%
20160405-0032680 54
 
0.1%
20140417-0032155 54
 
0.1%
20151104-0112572 53
 
0.1%
20141216-0115062 53
 
0.1%
Other values (52926) 55964
98.7%
2025-06-20T15:37:19.885691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 262374
29.0%
1 141748
15.6%
2 116933
12.9%
- 56642
 
6.3%
5 56506
 
6.2%
6 53870
 
5.9%
4 52509
 
5.8%
3 50583
 
5.6%
7 38570
 
4.3%
8 38276
 
4.2%
Other values (2) 38170
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 906181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 262374
29.0%
1 141748
15.6%
2 116933
12.9%
- 56642
 
6.3%
5 56506
 
6.2%
6 53870
 
5.9%
4 52509
 
5.8%
3 50583
 
5.6%
7 38570
 
4.3%
8 38276
 
4.2%
Other values (2) 38170
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 906181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 262374
29.0%
1 141748
15.6%
2 116933
12.9%
- 56642
 
6.3%
5 56506
 
6.2%
6 53870
 
5.9%
4 52509
 
5.8%
3 50583
 
5.6%
7 38570
 
4.3%
8 38276
 
4.2%
Other values (2) 38170
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 906181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 262374
29.0%
1 141748
15.6%
2 116933
12.9%
- 56642
 
6.3%
5 56506
 
6.2%
6 53870
 
5.9%
4 52509
 
5.8%
3 50583
 
5.6%
7 38570
 
4.3%
8 38276
 
4.2%
Other values (2) 38170
 
4.2%

Sold As Vacant
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.4 KiB
False
51741 
True
 
4895
ValueCountFrequency (%)
False 51741
91.4%
True 4895
 
8.6%
2025-06-20T15:37:20.135521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Multiple Parcels Involved in Sale
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.4 KiB
False
51947 
True
 
4689
ValueCountFrequency (%)
False 51947
91.7%
True 4689
 
8.3%
2025-06-20T15:37:20.280538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Owner Name
Text

Missing 

Distinct19713
Distinct (%)78.0%
Missing31375
Missing (%)55.4%
Memory size2.7 MiB
2025-06-20T15:37:20.685772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length60
Median length49
Mean length24.69447
Min length6

Characters and Unicode

Total characters623807
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15826 ?
Unique (%)62.6%

Sample

1st rowSTINSON, LAURA M.
2nd rowNUNES, JARED R.
3rd rowWHITFORD, KAREN
4th rowHENDERSON, JAMES P. & LYNN P.
5th rowMILLER, JORDAN
ValueCountFrequency (%)
12696
 
11.9%
llc 2125
 
2.0%
m 1576
 
1.5%
a 1551
 
1.5%
l 1402
 
1.3%
j 1088
 
1.0%
d 976
 
0.9%
r 974
 
0.9%
e 958
 
0.9%
c 864
 
0.8%
Other values (16340) 82709
77.4%
2025-06-20T15:37:21.490669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81693
 
13.1%
E 52823
 
8.5%
A 52373
 
8.4%
R 40686
 
6.5%
N 37410
 
6.0%
L 35076
 
5.6%
I 32439
 
5.2%
, 29245
 
4.7%
S 26468
 
4.2%
O 25497
 
4.1%
Other values (38) 210097
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 623807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81693
 
13.1%
E 52823
 
8.5%
A 52373
 
8.4%
R 40686
 
6.5%
N 37410
 
6.0%
L 35076
 
5.6%
I 32439
 
5.2%
, 29245
 
4.7%
S 26468
 
4.2%
O 25497
 
4.1%
Other values (38) 210097
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 623807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81693
 
13.1%
E 52823
 
8.5%
A 52373
 
8.4%
R 40686
 
6.5%
N 37410
 
6.0%
L 35076
 
5.6%
I 32439
 
5.2%
, 29245
 
4.7%
S 26468
 
4.2%
O 25497
 
4.1%
Other values (38) 210097
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 623807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81693
 
13.1%
E 52823
 
8.5%
A 52373
 
8.4%
R 40686
 
6.5%
N 37410
 
6.0%
L 35076
 
5.6%
I 32439
 
5.2%
, 29245
 
4.7%
S 26468
 
4.2%
O 25497
 
4.1%
Other values (38) 210097
33.7%

Address
Text

Missing 

Distinct22327
Distinct (%)85.8%
Missing30619
Missing (%)54.1%
Memory size2.6 MiB
2025-06-20T15:37:21.985589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length16.683015
Min length9

Characters and Unicode

Total characters434042
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19047 ?
Unique (%)73.2%

Sample

1st row1802 STEWART PL
2nd row2761 ROSEDALE PL
3rd row224 PEACHTREE ST
4th row316 LUTIE ST
5th row2626 FOSTER AVE
ValueCountFrequency (%)
dr 8311
 
9.8%
ave 7097
 
8.4%
st 3204
 
3.8%
rd 2358
 
2.8%
n 1405
 
1.7%
ct 1379
 
1.6%
ln 1007
 
1.2%
pl 572
 
0.7%
s 528
 
0.6%
blvd 515
 
0.6%
Other values (7213) 58318
68.9%
2025-06-20T15:37:22.748179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83994
19.4%
E 28844
 
6.6%
R 26458
 
6.1%
A 24262
 
5.6%
D 20125
 
4.6%
1 19676
 
4.5%
N 17166
 
4.0%
L 16149
 
3.7%
T 15499
 
3.6%
O 15213
 
3.5%
Other values (28) 166656
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 434042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
83994
19.4%
E 28844
 
6.6%
R 26458
 
6.1%
A 24262
 
5.6%
D 20125
 
4.6%
1 19676
 
4.5%
N 17166
 
4.0%
L 16149
 
3.7%
T 15499
 
3.6%
O 15213
 
3.5%
Other values (28) 166656
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 434042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
83994
19.4%
E 28844
 
6.6%
R 26458
 
6.1%
A 24262
 
5.6%
D 20125
 
4.6%
1 19676
 
4.5%
N 17166
 
4.0%
L 16149
 
3.7%
T 15499
 
3.6%
O 15213
 
3.5%
Other values (28) 166656
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 434042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
83994
19.4%
E 28844
 
6.6%
R 26458
 
6.1%
A 24262
 
5.6%
D 20125
 
4.6%
1 19676
 
4.5%
N 17166
 
4.0%
L 16149
 
3.7%
T 15499
 
3.6%
O 15213
 
3.5%
Other values (28) 166656
38.4%

City
Categorical

High correlation  Imbalance  Missing 

Distinct12
Distinct (%)< 0.1%
Missing30619
Missing (%)54.1%
Memory size3.1 MiB
NASHVILLE
20703 
ANTIOCH
 
1311
MADISON
 
1309
HERMITAGE
 
1055
OLD HICKORY
 
919
Other values (7)
 
720

Length

Max length14
Median length9
Mean length8.962832
Min length7

Characters and Unicode

Total characters233186
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNASHVILLE
2nd rowNASHVILLE
3rd rowNASHVILLE
4th rowNASHVILLE
5th rowNASHVILLE

Common Values

ValueCountFrequency (%)
NASHVILLE 20703
36.6%
ANTIOCH 1311
 
2.3%
MADISON 1309
 
2.3%
HERMITAGE 1055
 
1.9%
OLD HICKORY 919
 
1.6%
GOODLETTSVILLE 472
 
0.8%
BRENTWOOD 203
 
0.4%
WHITES CREEK 24
 
< 0.1%
JOELTON 11
 
< 0.1%
MOUNT JULIET 8
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 30619
54.1%

Length

2025-06-20T15:37:22.958121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nashville 20703
76.8%
antioch 1311
 
4.9%
madison 1309
 
4.9%
hermitage 1055
 
3.9%
old 919
 
3.4%
hickory 919
 
3.4%
goodlettsville 472
 
1.8%
brentwood 203
 
0.8%
whites 24
 
0.1%
creek 24
 
0.1%
Other values (5) 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
L 43765
18.8%
I 25802
11.1%
A 24378
10.5%
E 24056
10.3%
H 24012
10.3%
N 23547
10.1%
S 22509
9.7%
V 21177
9.1%
O 5839
 
2.5%
T 3564
 
1.5%
Other values (12) 14537
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 233186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 43765
18.8%
I 25802
11.1%
A 24378
10.5%
E 24056
10.3%
H 24012
10.3%
N 23547
10.1%
S 22509
9.7%
V 21177
9.1%
O 5839
 
2.5%
T 3564
 
1.5%
Other values (12) 14537
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 233186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 43765
18.8%
I 25802
11.1%
A 24378
10.5%
E 24056
10.3%
H 24012
10.3%
N 23547
10.1%
S 22509
9.7%
V 21177
9.1%
O 5839
 
2.5%
T 3564
 
1.5%
Other values (12) 14537
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 233186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 43765
18.8%
I 25802
11.1%
A 24378
10.5%
E 24056
10.3%
H 24012
10.3%
N 23547
10.1%
S 22509
9.7%
V 21177
9.1%
O 5839
 
2.5%
T 3564
 
1.5%
Other values (12) 14537
 
6.2%

State
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing30619
Missing (%)54.1%
Memory size2.9 MiB
TN
26017 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters52034
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTN
2nd rowTN
3rd rowTN
4th rowTN
5th rowTN

Common Values

ValueCountFrequency (%)
TN 26017
45.9%
(Missing) 30619
54.1%

Length

2025-06-20T15:37:23.161813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T15:37:23.349565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tn 26017
100.0%

Most occurring characters

ValueCountFrequency (%)
T 26017
50.0%
N 26017
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 26017
50.0%
N 26017
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 26017
50.0%
N 26017
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 26017
50.0%
N 26017
50.0%

Acreage
Real number (ℝ)

Missing  Skewed 

Distinct519
Distinct (%)2.0%
Missing30619
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean0.49890302
Minimum0.01
Maximum160.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:23.553559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.12
Q10.18
median0.27
Q30.45
95-th percentile1.26
Maximum160.06
Range160.05
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation1.5703958
Coefficient of variation (CV)3.1476975
Kurtosis4471.1658
Mean0.49890302
Median Absolute Deviation (MAD)0.1
Skewness52.279277
Sum12979.96
Variance2.466143
MonotonicityNot monotonic
2025-06-20T15:37:23.812473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17 1859
 
3.3%
0.18 1194
 
2.1%
0.2 1019
 
1.8%
0.23 955
 
1.7%
0.22 822
 
1.5%
0.19 787
 
1.4%
0.25 762
 
1.3%
0.27 741
 
1.3%
0.34 716
 
1.3%
0.16 699
 
1.2%
Other values (509) 16463
29.1%
(Missing) 30619
54.1%
ValueCountFrequency (%)
0.01 4
 
< 0.1%
0.02 6
 
< 0.1%
0.03 6
 
< 0.1%
0.04 12
 
< 0.1%
0.05 22
 
< 0.1%
0.06 55
 
0.1%
0.07 69
 
0.1%
0.08 105
 
0.2%
0.09 400
0.7%
0.1 248
0.4%
ValueCountFrequency (%)
160.06 1
< 0.1%
68.79 1
< 0.1%
62.96 1
< 0.1%
51.34 1
< 0.1%
47.5 1
< 0.1%
41.24 1
< 0.1%
35.97 1
< 0.1%
35 1
< 0.1%
34.64 1
< 0.1%
33.9 1
< 0.1%

Tax District
Categorical

Imbalance  Missing 

Distinct7
Distinct (%)< 0.1%
Missing30619
Missing (%)54.1%
Memory size3.4 MiB
URBAN SERVICES DISTRICT
20026 
GENERAL SERVICES DISTRICT
4556 
CITY OF FOREST HILLS
 
407
CITY OF OAK HILL
 
393
CITY OF GOODLETTSVILLE
 
379
Other values (2)
 
256

Length

Max length25
Median length23
Mean length23.14283
Min length16

Characters and Unicode

Total characters602107
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBAN SERVICES DISTRICT
2nd rowCITY OF BERRY HILL
3rd rowURBAN SERVICES DISTRICT
4th rowURBAN SERVICES DISTRICT
5th rowURBAN SERVICES DISTRICT

Common Values

ValueCountFrequency (%)
URBAN SERVICES DISTRICT 20026
35.4%
GENERAL SERVICES DISTRICT 4556
 
8.0%
CITY OF FOREST HILLS 407
 
0.7%
CITY OF OAK HILL 393
 
0.7%
CITY OF GOODLETTSVILLE 379
 
0.7%
CITY OF BELLE MEADE 235
 
0.4%
CITY OF BERRY HILL 21
 
< 0.1%
(Missing) 30619
54.1%

Length

2025-06-20T15:37:24.120890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T15:37:24.408487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
services 24582
31.1%
district 24582
31.1%
urban 20026
25.3%
general 4556
 
5.8%
city 1435
 
1.8%
of 1435
 
1.8%
hill 414
 
0.5%
forest 407
 
0.5%
hills 407
 
0.5%
oak 393
 
0.5%
Other values (4) 870
 
1.1%

Most occurring characters

ValueCountFrequency (%)
I 76381
12.7%
S 74939
12.4%
R 74195
12.3%
E 60402
10.0%
53090
8.8%
T 51764
8.6%
C 50599
8.4%
A 25210
 
4.2%
D 25196
 
4.2%
V 24961
 
4.1%
Other values (11) 85370
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 602107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 76381
12.7%
S 74939
12.4%
R 74195
12.3%
E 60402
10.0%
53090
8.8%
T 51764
8.6%
C 50599
8.4%
A 25210
 
4.2%
D 25196
 
4.2%
V 24961
 
4.1%
Other values (11) 85370
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 602107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 76381
12.7%
S 74939
12.4%
R 74195
12.3%
E 60402
10.0%
53090
8.8%
T 51764
8.6%
C 50599
8.4%
A 25210
 
4.2%
D 25196
 
4.2%
V 24961
 
4.1%
Other values (11) 85370
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 602107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 76381
12.7%
S 74939
12.4%
R 74195
12.3%
E 60402
10.0%
53090
8.8%
T 51764
8.6%
C 50599
8.4%
A 25210
 
4.2%
D 25196
 
4.2%
V 24961
 
4.1%
Other values (11) 85370
14.2%

Neighborhood
Real number (ℝ)

Missing 

Distinct203
Distinct (%)0.8%
Missing30619
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean4356.2158
Minimum107
Maximum9530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:24.849962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile1127
Q13126
median3929
Q36228
95-th percentile9026
Maximum9530
Range9423
Interquartile range (IQR)3102

Descriptive statistics

Standard deviation2170.3483
Coefficient of variation (CV)0.49821872
Kurtosis-0.27158766
Mean4356.2158
Median Absolute Deviation (MAD)1403
Skewness0.4580909
Sum1.1333567 × 108
Variance4710411.6
MonotonicityNot monotonic
2025-06-20T15:37:25.230851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4026 729
 
1.3%
2326 636
 
1.1%
7328 592
 
1.0%
1226 525
 
0.9%
3628 510
 
0.9%
2026 504
 
0.9%
226 481
 
0.8%
3426 462
 
0.8%
2526 454
 
0.8%
6226 442
 
0.8%
Other values (193) 20682
36.5%
(Missing) 30619
54.1%
ValueCountFrequency (%)
107 3
 
< 0.1%
126 294
0.5%
226 481
0.8%
326 53
 
0.1%
1026 105
 
0.2%
1111 1
 
< 0.1%
1113 3
 
< 0.1%
1126 256
0.5%
1127 130
 
0.2%
1129 143
 
0.3%
ValueCountFrequency (%)
9530 47
 
0.1%
9529 205
0.4%
9528 85
 
0.2%
9527 23
 
< 0.1%
9526 33
 
0.1%
9328 93
 
0.2%
9327 88
 
0.2%
9326 203
0.4%
9226 425
0.8%
9126 13
 
< 0.1%

image
Path

Missing 

Distinct21831
Distinct (%)86.2%
Missing31301
Missing (%)55.3%
Memory size2.6 MiB
\61000\560001.JPG
 
4
\18000\785001.JPG
 
4
\71000\442001.JPG
 
4
\143000\337001.JPG
 
4
\111000\868001.JPG
 
4
Other values (21826)
25315 

Length

Max length18
Median length17
Mean length17.333136
Min length13

Characters and Unicode

Total characters439135
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18687 ?
Unique (%)73.8%

Sample

1st row\114000\910001.JPG
2nd row\131000\191001.JPG
3rd row\133000\721001.JPG
4th row\134000\474001.JPG
5th row\134000\656001.JPG

Common Values

ValueCountFrequency (%)
\61000\560001.JPG 4
 
< 0.1%
\18000\785001.JPG 4
 
< 0.1%
\71000\442001.JPG 4
 
< 0.1%
\143000\337001.JPG 4
 
< 0.1%
\111000\868001.JPG 4
 
< 0.1%
\73000\486001.JPG 4
 
< 0.1%
\150000\78001.JPG 4
 
< 0.1%
\61000\559001.JPG 4
 
< 0.1%
\48000\197001.JPG 4
 
< 0.1%
\74000\502001.JPG 4
 
< 0.1%
Other values (21821) 25295
44.7%
(Missing) 31301
55.3%

Length

2025-06-20T15:37:25.545563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
61000\560001.jpg 4
 
< 0.1%
25000\868001.jpg 4
 
< 0.1%
18000\785001.jpg 4
 
< 0.1%
36000\112001.jpg 4
 
< 0.1%
86000\701001.jpg 4
 
< 0.1%
50000\409001.jpg 4
 
< 0.1%
112000\363001.jpg 4
 
< 0.1%
250000\1001.jpg 4
 
< 0.1%
133000\892001.jpg 4
 
< 0.1%
62000\411001.jpg 4
 
< 0.1%
Other values (21821) 25295
99.8%

Most occurring characters

ValueCountFrequency (%)
0 135074
30.8%
\ 50670
 
11.5%
1 48217
 
11.0%
. 25335
 
5.8%
J 25335
 
5.8%
P 25335
 
5.8%
G 25335
 
5.8%
2 13432
 
3.1%
6 13403
 
3.1%
7 13359
 
3.0%
Other values (6) 63640
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 439135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 135074
30.8%
\ 50670
 
11.5%
1 48217
 
11.0%
. 25335
 
5.8%
J 25335
 
5.8%
P 25335
 
5.8%
G 25335
 
5.8%
2 13432
 
3.1%
6 13403
 
3.1%
7 13359
 
3.0%
Other values (6) 63640
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 439135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 135074
30.8%
\ 50670
 
11.5%
1 48217
 
11.0%
. 25335
 
5.8%
J 25335
 
5.8%
P 25335
 
5.8%
G 25335
 
5.8%
2 13432
 
3.1%
6 13403
 
3.1%
7 13359
 
3.0%
Other values (6) 63640
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 439135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 135074
30.8%
\ 50670
 
11.5%
1 48217
 
11.0%
. 25335
 
5.8%
J 25335
 
5.8%
P 25335
 
5.8%
G 25335
 
5.8%
2 13432
 
3.1%
6 13403
 
3.1%
7 13359
 
3.0%
Other values (6) 63640
14.5%
Common prefix\
Unique stems21831
Unique names1000
Unique extensions1
Unique directories261
Unique anchors1
ValueCountFrequency (%)
\61000\560001.JPG 4
 
< 0.1%
\18000\785001.JPG 4
 
< 0.1%
\71000\442001.JPG 4
 
< 0.1%
\143000\337001.JPG 4
 
< 0.1%
\111000\868001.JPG 4
 
< 0.1%
\73000\486001.JPG 4
 
< 0.1%
\150000\78001.JPG 4
 
< 0.1%
\61000\559001.JPG 4
 
< 0.1%
\48000\197001.JPG 4
 
< 0.1%
\74000\502001.JPG 4
 
< 0.1%
Other values (21821) 25295
44.7%
(Missing) 31301
55.3%
ValueCountFrequency (%)
\36000\736001 4
 
< 0.1%
\71000\442001 4
 
< 0.1%
\65000\441001 4
 
< 0.1%
\67000\746001 4
 
< 0.1%
\81000\943001 4
 
< 0.1%
\48000\188001 4
 
< 0.1%
\86000\701001 4
 
< 0.1%
\50000\409001 4
 
< 0.1%
\25000\868001 4
 
< 0.1%
\111000\868001 4
 
< 0.1%
Other values (21821) 25295
44.7%
(Missing) 31301
55.3%
ValueCountFrequency (%)
511001.JPG 41
 
0.1%
189001.JPG 41
 
0.1%
650001.JPG 40
 
0.1%
943001.JPG 40
 
0.1%
560001.JPG 40
 
0.1%
271001.JPG 40
 
0.1%
630001.JPG 40
 
0.1%
941001.JPG 39
 
0.1%
446001.JPG 39
 
0.1%
477001.JPG 39
 
0.1%
Other values (990) 24936
44.0%
(Missing) 31301
55.3%
ValueCountFrequency (%)
.JPG 25335
44.7%
(Missing) 31301
55.3%
ValueCountFrequency (%)
\82000 302
 
0.5%
\66000 300
 
0.5%
\48000 277
 
0.5%
\73000 277
 
0.5%
\93000 267
 
0.5%
\52000 263
 
0.5%
\72000 263
 
0.5%
\50000 257
 
0.5%
\36000 256
 
0.5%
\51000 255
 
0.5%
Other values (251) 22618
39.9%
(Missing) 31301
55.3%
ValueCountFrequency (%)
25335
44.7%
(Missing) 31301
55.3%

Land Value
Real number (ℝ)

High correlation  Missing 

Distinct1122
Distinct (%)4.3%
Missing30619
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean69072.665
Minimum100
Maximum2772000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:25.793449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile12000
Q121000
median28800
Q360000
95-th percentile250000
Maximum2772000
Range2771900
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation106040.53
Coefficient of variation (CV)1.5352026
Kurtosis50.714073
Mean69072.665
Median Absolute Deviation (MAD)11200
Skewness5.0420974
Sum1.7970635 × 109
Variance1.1244595 × 1010
MonotonicityNot monotonic
2025-06-20T15:37:26.031914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 1534
 
2.7%
26000 1038
 
1.8%
27000 887
 
1.6%
30000 849
 
1.5%
18000 812
 
1.4%
24000 742
 
1.3%
45000 669
 
1.2%
15000 669
 
1.2%
11000 626
 
1.1%
22000 624
 
1.1%
Other values (1112) 17567
31.0%
(Missing) 30619
54.1%
ValueCountFrequency (%)
100 3
 
< 0.1%
200 9
< 0.1%
300 1
 
< 0.1%
400 1
 
< 0.1%
500 8
 
< 0.1%
600 5
 
< 0.1%
800 9
< 0.1%
900 5
 
< 0.1%
1000 21
< 0.1%
1200 1
 
< 0.1%
ValueCountFrequency (%)
2772000 1
< 0.1%
1921700 1
< 0.1%
1869000 1
< 0.1%
1830700 1
< 0.1%
1603800 1
< 0.1%
1567600 1
< 0.1%
1392800 2
< 0.1%
1276000 1
< 0.1%
1264000 1
< 0.1%
1255500 1
< 0.1%

Building Value
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct4406
Distinct (%)16.9%
Missing30619
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean160802.46
Minimum0
Maximum12971800
Zeros1852
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:26.263948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175900
median111400
Q3180700
95-th percentile448400
Maximum12971800
Range12971800
Interquartile range (IQR)104800

Descriptive statistics

Standard deviation206804.06
Coefficient of variation (CV)1.2860752
Kurtosis635.2196
Mean160802.46
Median Absolute Deviation (MAD)45500
Skewness14.219915
Sum4.1835976 × 109
Variance4.2767919 × 1010
MonotonicityNot monotonic
2025-06-20T15:37:26.883508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1852
 
3.3%
88000 37
 
0.1%
70000 35
 
0.1%
86300 34
 
0.1%
81000 33
 
0.1%
98900 33
 
0.1%
102900 33
 
0.1%
102500 31
 
0.1%
90600 31
 
0.1%
88200 31
 
0.1%
Other values (4396) 23867
42.1%
(Missing) 30619
54.1%
ValueCountFrequency (%)
0 1852
3.3%
1400 1
 
< 0.1%
1600 1
 
< 0.1%
2100 1
 
< 0.1%
2300 1
 
< 0.1%
2700 1
 
< 0.1%
2900 2
 
< 0.1%
3300 1
 
< 0.1%
3400 2
 
< 0.1%
3500 1
 
< 0.1%
ValueCountFrequency (%)
12971800 1
< 0.1%
5824300 2
< 0.1%
3768000 1
< 0.1%
3563100 1
< 0.1%
3456900 1
< 0.1%
2691500 1
< 0.1%
2673700 1
< 0.1%
2493900 1
< 0.1%
2490600 1
< 0.1%
2472500 1
< 0.1%

Total Value
Real number (ℝ)

High correlation  Missing 

Distinct5848
Distinct (%)22.5%
Missing30619
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean232397.09
Minimum100
Maximum13940400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:27.230856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile37600
Q1102800
median148500
Q3268500
95-th percentile672760
Maximum13940400
Range13940300
Interquartile range (IQR)165700

Descriptive statistics

Standard deviation281070.27
Coefficient of variation (CV)1.2094397
Kurtosis261.93563
Mean232397.09
Median Absolute Deviation (MAD)62800
Skewness8.9648397
Sum6.0462752 × 109
Variance7.9000495 × 1010
MonotonicityNot monotonic
2025-06-20T15:37:27.757765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 173
 
0.3%
25000 93
 
0.2%
40000 83
 
0.1%
13000 79
 
0.1%
20000 73
 
0.1%
45000 62
 
0.1%
18000 45
 
0.1%
30000 43
 
0.1%
15000 43
 
0.1%
35000 31
 
0.1%
Other values (5838) 25292
44.7%
(Missing) 30619
54.1%
ValueCountFrequency (%)
100 2
 
< 0.1%
200 9
< 0.1%
300 1
 
< 0.1%
400 1
 
< 0.1%
500 8
 
< 0.1%
600 5
 
< 0.1%
800 9
< 0.1%
900 3
 
< 0.1%
1000 21
< 0.1%
1200 1
 
< 0.1%
ValueCountFrequency (%)
13940400 1
< 0.1%
6402600 2
< 0.1%
5697100 1
< 0.1%
4455200 1
< 0.1%
4058100 1
< 0.1%
3723900 1
< 0.1%
3500000 1
< 0.1%
3388400 1
< 0.1%
3290300 1
< 0.1%
3157400 1
< 0.1%

Finished Area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct6117
Distinct (%)25.3%
Missing32470
Missing (%)57.3%
Infinite0
Infinite (%)0.0%
Mean1926.9543
Minimum0
Maximum197988
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:27.982109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile864
Q11239
median1632
Q32212
95-th percentile3986.7225
Maximum197988
Range197988
Interquartile range (IQR)973

Descriptive statistics

Standard deviation1687.0173
Coefficient of variation (CV)0.8754838
Kurtosis7553.5889
Mean1926.9543
Median Absolute Deviation (MAD)460.5
Skewness65.945117
Sum46566779
Variance2846027.4
MonotonicityNot monotonic
2025-06-20T15:37:28.280961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 114
 
0.2%
1200 113
 
0.2%
768 112
 
0.2%
1000 102
 
0.2%
975 100
 
0.2%
1350 95
 
0.2%
1050 92
 
0.2%
1650 91
 
0.2%
960 82
 
0.1%
1500 78
 
0.1%
Other values (6107) 23187
40.9%
(Missing) 32470
57.3%
ValueCountFrequency (%)
0 2
< 0.1%
450 1
< 0.1%
463 1
< 0.1%
480 1
< 0.1%
504 1
< 0.1%
520 1
< 0.1%
528 1
< 0.1%
540 1
< 0.1%
560 1
< 0.1%
569 1
< 0.1%
ValueCountFrequency (%)
197988 1
< 0.1%
25193 1
< 0.1%
19728.24988 1
< 0.1%
16372 1
< 0.1%
15574 1
< 0.1%
15446 2
< 0.1%
15378 2
< 0.1%
15375 1
< 0.1%
12740 1
< 0.1%
12714.66003 1
< 0.1%

Foundation Type
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing32472
Missing (%)57.3%
Memory size3.0 MiB
CRAWL
15389 
FULL BSMT
3917 
PT BSMT
3200 
SLAB
1581 
TYPICAL
 
40

Length

Max length9
Median length5
Mean length5.8511422
Min length4

Characters and Unicode

Total characters141387
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPT BSMT
2nd rowSLAB
3rd rowFULL BSMT
4th rowCRAWL
5th rowCRAWL

Common Values

ValueCountFrequency (%)
CRAWL 15389
27.2%
FULL BSMT 3917
 
6.9%
PT BSMT 3200
 
5.7%
SLAB 1581
 
2.8%
TYPICAL 40
 
0.1%
PIERS 37
 
0.1%
(Missing) 32472
57.3%

Length

2025-06-20T15:37:28.525467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T15:37:28.714139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
crawl 15389
49.2%
bsmt 7117
22.8%
full 3917
 
12.5%
pt 3200
 
10.2%
slab 1581
 
5.1%
typical 40
 
0.1%
piers 37
 
0.1%

Most occurring characters

ValueCountFrequency (%)
L 24844
17.6%
A 17010
12.0%
C 15429
10.9%
R 15426
10.9%
W 15389
10.9%
T 10357
7.3%
S 8735
 
6.2%
B 8698
 
6.2%
7117
 
5.0%
M 7117
 
5.0%
Other values (6) 11265
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 24844
17.6%
A 17010
12.0%
C 15429
10.9%
R 15426
10.9%
W 15389
10.9%
T 10357
7.3%
S 8735
 
6.2%
B 8698
 
6.2%
7117
 
5.0%
M 7117
 
5.0%
Other values (6) 11265
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 24844
17.6%
A 17010
12.0%
C 15429
10.9%
R 15426
10.9%
W 15389
10.9%
T 10357
7.3%
S 8735
 
6.2%
B 8698
 
6.2%
7117
 
5.0%
M 7117
 
5.0%
Other values (6) 11265
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 24844
17.6%
A 17010
12.0%
C 15429
10.9%
R 15426
10.9%
W 15389
10.9%
T 10357
7.3%
S 8735
 
6.2%
B 8698
 
6.2%
7117
 
5.0%
M 7117
 
5.0%
Other values (6) 11265
8.0%

Year Built
Real number (ℝ)

Missing 

Distinct126
Distinct (%)0.5%
Missing32471
Missing (%)57.3%
Infinite0
Infinite (%)0.0%
Mean1963.7492
Minimum1799
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:29.009294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1799
5-th percentile1924
Q11948
median1960
Q31983
95-th percentile2015
Maximum2017
Range218
Interquartile range (IQR)35

Descriptive statistics

Standard deviation26.546141
Coefficient of variation (CV)0.013518091
Kurtosis-0.3264249
Mean1963.7492
Median Absolute Deviation (MAD)16
Skewness0.27503388
Sum47454000
Variance704.69758
MonotonicityNot monotonic
2025-06-20T15:37:29.280972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 1153
 
2.0%
1930 1008
 
1.8%
1955 1005
 
1.8%
1960 759
 
1.3%
1940 751
 
1.3%
2015 712
 
1.3%
2016 602
 
1.1%
1920 561
 
1.0%
1948 538
 
0.9%
1958 536
 
0.9%
Other values (116) 16540
29.2%
(Missing) 32471
57.3%
ValueCountFrequency (%)
1799 1
 
< 0.1%
1832 1
 
< 0.1%
1870 2
 
< 0.1%
1880 1
 
< 0.1%
1890 1
 
< 0.1%
1893 1
 
< 0.1%
1894 1
 
< 0.1%
1899 93
0.2%
1900 72
0.1%
1901 1
 
< 0.1%
ValueCountFrequency (%)
2017 13
 
< 0.1%
2016 602
1.1%
2015 712
1.3%
2014 526
0.9%
2013 297
0.5%
2012 71
 
0.1%
2011 32
 
0.1%
2010 36
 
0.1%
2009 47
 
0.1%
2008 68
 
0.1%

Exterior Wall
Categorical

Imbalance  Missing 

Distinct10
Distinct (%)< 0.1%
Missing32471
Missing (%)57.3%
Memory size3.0 MiB
BRICK
11942 
FRAME
8870 
BRICK/FRAME
2602 
STONE
 
331
STUCCO
 
168
Other values (5)
 
252

Length

Max length12
Median length5
Mean length5.692903
Min length3

Characters and Unicode

Total characters137569
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBRICK
2nd rowBRICK/FRAME
3rd rowBRICK/FRAME
4th rowFRAME
5th rowFRAME

Common Values

ValueCountFrequency (%)
BRICK 11942
 
21.1%
FRAME 8870
 
15.7%
BRICK/FRAME 2602
 
4.6%
STONE 331
 
0.6%
STUCCO 168
 
0.3%
CONC BLK 113
 
0.2%
FRAME/STONE 108
 
0.2%
LOG 15
 
< 0.1%
METAL 15
 
< 0.1%
PRECAST CONC 1
 
< 0.1%
(Missing) 32471
57.3%

Length

2025-06-20T15:37:29.495583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T15:37:29.675876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
brick 11942
49.2%
frame 8870
36.5%
brick/frame 2602
 
10.7%
stone 331
 
1.4%
stucco 168
 
0.7%
conc 114
 
0.5%
blk 113
 
0.5%
frame/stone 108
 
0.4%
log 15
 
0.1%
metal 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 26125
19.0%
C 15109
11.0%
B 14657
10.7%
K 14657
10.7%
I 14544
10.6%
E 12035
8.7%
A 11596
8.4%
M 11595
8.4%
F 11580
8.4%
/ 2710
 
2.0%
Other values (9) 2961
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 26125
19.0%
C 15109
11.0%
B 14657
10.7%
K 14657
10.7%
I 14544
10.6%
E 12035
8.7%
A 11596
8.4%
M 11595
8.4%
F 11580
8.4%
/ 2710
 
2.0%
Other values (9) 2961
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 26125
19.0%
C 15109
11.0%
B 14657
10.7%
K 14657
10.7%
I 14544
10.6%
E 12035
8.7%
A 11596
8.4%
M 11595
8.4%
F 11580
8.4%
/ 2710
 
2.0%
Other values (9) 2961
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 26125
19.0%
C 15109
11.0%
B 14657
10.7%
K 14657
10.7%
I 14544
10.6%
E 12035
8.7%
A 11596
8.4%
M 11595
8.4%
F 11580
8.4%
/ 2710
 
2.0%
Other values (9) 2961
 
2.2%

Grade
Categorical

High correlation  Imbalance  Missing 

Distinct20
Distinct (%)0.1%
Missing32471
Missing (%)57.3%
Memory size3.0 MiB
C
17262 
B
3698 
D
1984 
A
 
598
X
 
523
Other values (15)
 
100

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters96660
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 17262
30.5%
B 3698
 
6.5%
D 1984
 
3.5%
A 598
 
1.1%
X 523
 
0.9%
E 60
 
0.1%
TCC 20
 
< 0.1%
AAB 3
 
< 0.1%
IDC 3
 
< 0.1%
AAC 3
 
< 0.1%
Other values (10) 11
 
< 0.1%
(Missing) 32471
57.3%

Length

2025-06-20T15:37:29.948108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c 17262
71.4%
b 3698
 
15.3%
d 1984
 
8.2%
a 598
 
2.5%
x 523
 
2.2%
e 60
 
0.2%
tcc 20
 
0.1%
aab 3
 
< 0.1%
idc 3
 
< 0.1%
aac 3
 
< 0.1%
Other values (10) 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
72415
74.9%
C 17316
 
17.9%
B 3704
 
3.8%
D 1989
 
2.1%
A 611
 
0.6%
X 523
 
0.5%
E 60
 
0.1%
T 24
 
< 0.1%
O 4
 
< 0.1%
F 4
 
< 0.1%
Other values (4) 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
72415
74.9%
C 17316
 
17.9%
B 3704
 
3.8%
D 1989
 
2.1%
A 611
 
0.6%
X 523
 
0.5%
E 60
 
0.1%
T 24
 
< 0.1%
O 4
 
< 0.1%
F 4
 
< 0.1%
Other values (4) 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
72415
74.9%
C 17316
 
17.9%
B 3704
 
3.8%
D 1989
 
2.1%
A 611
 
0.6%
X 523
 
0.5%
E 60
 
0.1%
T 24
 
< 0.1%
O 4
 
< 0.1%
F 4
 
< 0.1%
Other values (4) 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
72415
74.9%
C 17316
 
17.9%
B 3704
 
3.8%
D 1989
 
2.1%
A 611
 
0.6%
X 523
 
0.5%
E 60
 
0.1%
T 24
 
< 0.1%
O 4
 
< 0.1%
F 4
 
< 0.1%
Other values (4) 10
 
< 0.1%

Bedrooms
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)< 0.1%
Missing32477
Missing (%)57.3%
Infinite0
Infinite (%)0.0%
Mean3.0900286
Minimum0
Maximum11
Zeros43
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:30.162577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q33
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.852942
Coefficient of variation (CV)0.27603046
Kurtosis3.0466984
Mean3.0900286
Median Absolute Deviation (MAD)0
Skewness0.8779929
Sum74652
Variance0.72751005
MonotonicityNot monotonic
2025-06-20T15:37:30.380155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 12877
 
22.7%
2 5104
 
9.0%
4 4857
 
8.6%
5 871
 
1.5%
6 243
 
0.4%
1 102
 
0.2%
0 43
 
0.1%
7 35
 
0.1%
8 22
 
< 0.1%
10 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
(Missing) 32477
57.3%
ValueCountFrequency (%)
0 43
 
0.1%
1 102
 
0.2%
2 5104
 
9.0%
3 12877
22.7%
4 4857
 
8.6%
5 871
 
1.5%
6 243
 
0.4%
7 35
 
0.1%
8 22
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 22
 
< 0.1%
7 35
 
0.1%
6 243
 
0.4%
5 871
 
1.5%
4 4857
 
8.6%
3 12877
22.7%
2 5104
 
9.0%

Full Bath
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)< 0.1%
Missing32359
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean1.8861062
Minimum0
Maximum10
Zeros164
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size442.6 KiB
2025-06-20T15:37:30.601114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96157181
Coefficient of variation (CV)0.50981849
Kurtosis3.3876773
Mean1.8861062
Median Absolute Deviation (MAD)1
Skewness1.3481894
Sum45789
Variance0.92462035
MonotonicityNot monotonic
2025-06-20T15:37:30.813278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 9875
 
17.4%
1 9359
 
16.5%
3 3446
 
6.1%
4 990
 
1.7%
5 329
 
0.6%
0 164
 
0.3%
6 85
 
0.2%
7 16
 
< 0.1%
8 6
 
< 0.1%
10 4
 
< 0.1%
(Missing) 32359
57.1%
ValueCountFrequency (%)
0 164
 
0.3%
1 9359
16.5%
2 9875
17.4%
3 3446
 
6.1%
4 990
 
1.7%
5 329
 
0.6%
6 85
 
0.2%
7 16
 
< 0.1%
8 6
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 4
 
< 0.1%
9 3
 
< 0.1%
8 6
 
< 0.1%
7 16
 
< 0.1%
6 85
 
0.2%
5 329
 
0.6%
4 990
 
1.7%
3 3446
 
6.1%
2 9875
17.4%
1 9359
16.5%

Half Bath
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing32490
Missing (%)57.4%
Memory size2.9 MiB
0.0
17683 
1.0
6094 
2.0
 
344
3.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72438
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17683
31.2%
1.0 6094
 
10.8%
2.0 344
 
0.6%
3.0 25
 
< 0.1%
(Missing) 32490
57.4%

Length

2025-06-20T15:37:31.037936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T15:37:31.227381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17683
73.2%
1.0 6094
 
25.2%
2.0 344
 
1.4%
3.0 25
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 41829
57.7%
. 24146
33.3%
1 6094
 
8.4%
2 344
 
0.5%
3 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 41829
57.7%
. 24146
33.3%
1 6094
 
8.4%
2 344
 
0.5%
3 25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 41829
57.7%
. 24146
33.3%
1 6094
 
8.4%
2 344
 
0.5%
3 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 41829
57.7%
. 24146
33.3%
1 6094
 
8.4%
2 344
 
0.5%
3 25
 
< 0.1%

Interactions

2025-06-20T15:37:08.245633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:45.991591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:47.900823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:50.627968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:52.547806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:54.751729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:56.710422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:58.568042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:00.558120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:02.430851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:04.725998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:06.495977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:08.392551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:46.195856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:48.045779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:50.783579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:52.727446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:54.919569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:56.900861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:58.725583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:00.700817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:02.675852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:04.870827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:06.642006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:08.542441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:46.341003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:48.185886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:50.935855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:52.946240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:55.085627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:57.045754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:58.963648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:00.850840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:02.842224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:05.020654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:06.785559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:08.700768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:46.491325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:48.330675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:51.080635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:53.135809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:55.241197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:57.200785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:59.135834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:01.008217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:03.009161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:05.165813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:06.925623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:08.845842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:46.677098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:48.576252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:51.245837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:53.295897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:55.400649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:57.361961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:59.310255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:01.175897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:03.162631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:05.295587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:07.068046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:08.984571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:46.830778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:48.781702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:51.395918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:53.445572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:55.544457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:57.495583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:59.460568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:01.342844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:03.329839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:05.450978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:07.209338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:09.140844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:46.981017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:49.050640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:51.535631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:53.600760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:55.688005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:57.649966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:59.608878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:01.498074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:03.500686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:05.615797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:07.375514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:09.285751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:47.175871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:49.519683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:51.695658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:53.765746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:55.835854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:57.800774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:59.759552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:01.650942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:03.895937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:05.765663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:07.519601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:09.545853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:47.330764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:49.791314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:51.867829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:53.926019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:56.000005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:57.950731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:59.925766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:01.815724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:04.065848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:05.919760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:07.666680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:09.710164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:47.493585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:49.993900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:52.075766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:54.091966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:56.212043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:58.126033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:00.100740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:01.985611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:04.225673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:06.080831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:07.835705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:09.859289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:47.629829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:50.189915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:52.215826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:54.449757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:56.380987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:58.265792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:00.249997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:02.142171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:04.400959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:06.209213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:07.966772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:10.015648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:47.763400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:50.417782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:52.379148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:54.601824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:56.524748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:36:58.415524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:00.395885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:02.281888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:04.565777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:06.350941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T15:37:08.108983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-20T15:37:31.417648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AcreageBedroomsBuilding ValueCityExterior WallFinished AreaFoundation TypeFull BathGradeHalf BathLand UseLand ValueMultiple Parcels Involved in SaleNeighborhoodProperty CitySale PriceSold As VacantTax DistrictTotal ValueUnnamed: 0Unnamed: 0.1Year Built
Acreage1.0000.2110.2160.4480.0190.3670.0790.1540.4080.0060.2880.3790.0550.4920.4480.2700.0600.0000.299-0.009-0.0090.038
Bedrooms0.2111.0000.4650.0670.0800.6300.2210.5790.2360.1660.2640.3200.0770.0670.0670.3600.0840.1100.442-0.040-0.0400.195
Building Value0.2160.4651.0000.0000.0810.7860.0820.6200.5430.1620.0830.6060.0240.0050.0000.6430.0000.1270.916-0.131-0.1310.115
City0.4480.0670.0001.0000.0920.0000.0920.0500.1040.0530.0880.2210.0660.4041.0000.0000.0530.4990.0310.0220.0220.167
Exterior Wall0.0190.0800.0810.0921.0000.0000.1600.0780.1640.0660.1080.0690.0500.1710.0920.0200.1210.0810.0820.0090.0090.232
Finished Area0.3670.6300.7860.0000.0001.0000.1570.6990.7240.0000.1740.5750.0380.0800.0000.6190.0000.0000.772-0.067-0.0670.197
Foundation Type0.0790.2210.0820.0920.1600.1571.0000.0840.4600.0370.3570.0740.0840.1250.0920.1120.0550.0750.0770.0130.0130.150
Full Bath0.1540.5790.6200.0500.0780.6990.0841.0000.2710.1730.1020.4140.0220.0480.0500.4650.0860.1860.590-0.062-0.0620.291
Grade0.4080.2360.5430.1040.1640.7240.4600.2711.0000.2730.5790.2520.1270.2180.1040.7070.1730.2300.5300.0260.0260.176
Half Bath0.0060.1660.1620.0530.0660.0000.0370.1730.2731.0000.1020.1390.0100.1010.0530.0000.0980.1030.1970.0210.0210.185
Land Use0.2880.2640.0830.0880.1080.1740.3570.1020.5790.1021.0000.1180.5440.0990.0650.1230.7890.0630.0760.1120.1120.217
Land Value0.3790.3200.6060.2210.0690.5750.0740.4140.2520.1390.1181.0000.0130.0280.2210.7520.0490.3670.802-0.093-0.093-0.072
Multiple Parcels Involved in Sale0.0550.0770.0240.0660.0500.0380.0840.0220.1270.0100.5440.0131.0000.1150.1330.1760.5090.0240.0270.0610.0610.076
Neighborhood0.4920.0670.0050.4040.1710.0800.1250.0480.2180.1010.0990.0280.1151.0000.404-0.0550.1390.4920.0120.0170.0170.195
Property City0.4480.0670.0001.0000.0920.0000.0920.0500.1040.0530.0650.2210.1330.4041.0000.0250.1470.4990.0310.0390.0390.167
Sale Price0.2700.3600.6430.0000.0200.6190.1120.4650.7070.0000.1230.7520.176-0.0550.0251.0000.0150.0070.7630.1210.121-0.057
Sold As Vacant0.0600.0840.0000.0530.1210.0000.0550.0860.1730.0980.7890.0490.5090.1390.1470.0151.0000.0480.0000.0620.0620.325
Tax District0.0000.1100.1270.4990.0810.0000.0750.1860.2300.1030.0630.3670.0240.4920.4990.0070.0481.0000.1800.0230.0230.128
Total Value0.2990.4420.9160.0310.0820.7720.0770.5900.5300.1970.0760.8020.0270.0120.0310.7630.0000.1801.000-0.124-0.1240.044
Unnamed: 0-0.009-0.040-0.1310.0220.009-0.0670.013-0.0620.0260.0210.112-0.0930.0610.0170.0390.1210.0620.023-0.1241.0001.0000.017
Unnamed: 0.1-0.009-0.040-0.1310.0220.009-0.0670.013-0.0620.0260.0210.112-0.0930.0610.0170.0390.1210.0620.023-0.1241.0001.0000.017
Year Built0.0380.1950.1150.1670.2320.1970.1500.2910.1760.1850.217-0.0720.0760.1950.167-0.0570.3250.1280.0440.0170.0171.000

Missing values

2025-06-20T15:37:10.324116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-20T15:37:11.099731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-20T15:37:12.215963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0.1Unnamed: 0Parcel IDLand UseProperty AddressSuite/ Condo #Property CitySale DateSale PriceLegal ReferenceSold As VacantMultiple Parcels Involved in SaleOwner NameAddressCityStateAcreageTax DistrictNeighborhoodimageLand ValueBuilding ValueTotal ValueFinished AreaFoundation TypeYear BuiltExterior WallGradeBedroomsFull BathHalf Bath
000105 03 0D 008.00RESIDENTIAL CONDO1208 3RD AVE S8NASHVILLE2013-01-2413200020130128-0008725NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
111105 11 0 080.00SINGLE FAMILY1802 STEWART PLNaNNASHVILLE2013-01-1119150020130118-0006337NoNoSTINSON, LAURA M.1802 STEWART PLNASHVILLETN0.17URBAN SERVICES DISTRICT3127.0\114000\910001.JPG32000.0134400.0168300.01149.00000PT BSMT1941.0BRICKC2.01.00.0
222118 03 0 130.00SINGLE FAMILY2761 ROSEDALE PLNaNNASHVILLE2013-01-1820200020130124-0008033NoNoNUNES, JARED R.2761 ROSEDALE PLNASHVILLETN0.11CITY OF BERRY HILL9126.0\131000\191001.JPG34000.0157800.0191800.02090.82495SLAB2000.0BRICK/FRAMEC3.02.01.0
333119 01 0 479.00SINGLE FAMILY224 PEACHTREE STNaNNASHVILLE2013-01-183200020130128-0008863NoNoWHITFORD, KAREN224 PEACHTREE STNASHVILLETN0.17URBAN SERVICES DISTRICT3130.0\133000\721001.JPG25000.0243700.0268700.02145.60001FULL BSMT1948.0BRICK/FRAMEB4.02.00.0
444119 05 0 186.00SINGLE FAMILY316 LUTIE STNaNNASHVILLE2013-01-2310200020130131-0009929NoNoHENDERSON, JAMES P. & LYNN P.316 LUTIE STNASHVILLETN0.34URBAN SERVICES DISTRICT3130.0\134000\474001.JPG25000.0138100.0164800.01969.00000CRAWL1910.0FRAMEC2.01.00.0
555119 05 0 387.00SINGLE FAMILY2626 FOSTER AVENaNNASHVILLE2013-01-049373620130118-0006110NoNoMILLER, JORDAN2626 FOSTER AVENASHVILLETN0.17URBAN SERVICES DISTRICT3130.0\134000\656001.JPG25000.086100.0113300.01037.00000CRAWL1945.0FRAMEC2.01.00.0
666119 10 0A 104.00RESIDENTIAL CONDO104 PRESCOTT PLNaNNASHVILLE2013-01-076490020130109-0002881NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
777119 13 0 183.00SINGLE FAMILY501 MORTON AVENaNNASHVILLE2013-01-154400020130115-0004888NoNoMICKLER, PATRICK L. & LOIS J. & ARNETT, RYAN D.501 MORTON AVENASHVILLETN0.20URBAN SERVICES DISTRICT3179.0\136000\266001.JPG16000.068100.084300.01216.00000CRAWL1932.0FRAMED2.01.00.0
888119 13 0 183.00SINGLE FAMILY501 MORTON AVENaNNASHVILLE2013-01-254990020130128-0008950NoNoMICKLER, PATRICK L. & LOIS J. & ARNETT, RYAN D.501 MORTON AVENASHVILLETN0.20URBAN SERVICES DISTRICT3179.0\136000\266001.JPG16000.068100.084300.01216.00000CRAWL1932.0FRAMED2.01.00.0
999119 15 0 158.00SINGLE FAMILY113 NEESE DRNaNNASHVILLE2013-01-092500020130111-0003850NoNoSONA LAND CO, LLC113 NEESE DRNASHVILLETN0.40URBAN SERVICES DISTRICT3131.0\137000\81001.JPG25000.057100.088400.01152.00000CRAWL1945.0FRAMEC2.01.00.0
Unnamed: 0.1Unnamed: 0Parcel IDLand UseProperty AddressSuite/ Condo #Property CitySale DateSale PriceLegal ReferenceSold As VacantMultiple Parcels Involved in SaleOwner NameAddressCityStateAcreageTax DistrictNeighborhoodimageLand ValueBuilding ValueTotal ValueFinished AreaFoundation TypeYear BuiltExterior WallGradeBedroomsFull BathHalf Bath
566265662656626093 06 0A 004.00RESIDENTIAL CONDO301 DEMONBREUN ST201.0NASHVILLE2016-10-2032500020161027-0113759NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566275662756627093 09 0C 090.00RESIDENTIAL CONDO1212 LAUREL ST1003.0NASHVILLE2016-10-2158990020161114-0119528NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566285662856628093 09 0C 105.00RESIDENTIAL CONDO1212 LAUREL ST1103.0NASHVILLE2016-10-1154300020161014-0108683NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566295662956629093 09 0C 262.00RESIDENTIAL CONDO1212 LAUREL ST2110.0NASHVILLE2016-10-0339700020161006-0105844NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566305663056630093 11 0B 012.00RESIDENTIAL CONDO464 2ND AVE SNaNNASHVILLE2016-10-1427500020161018-0109872NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566315663156631093 13 0B 274.00RESIDENTIAL CONDO320 11TH AVE S274.0NASHVILLE2016-10-0621000020161007-0106599NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566325663256632093 13 0D 044.00RESIDENTIAL CONDO700 12TH AVE S608.0NASHVILLE2016-10-2533800020161101-0115186NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566335663356633093 13 0D 048.00RESIDENTIAL CONDO700 12TH AVE S613.0NASHVILLE2016-10-0474200020161010-0106889NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566345663456634093 13 0D 056.00RESIDENTIAL CONDO700 12TH AVE S708.0NASHVILLE2016-10-2632000020161031-0114730NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566355663556635093 13 0D 094.00RESIDENTIAL CONDO700 12TH AVE S1008.0NASHVILLE2016-10-2733000020161104-0117077NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN